Data Visualization

Row

MAXIMUM KILLED

107069

MINIMUM KILLED

4

AVERAGE KILLLED

20353.28

TOTAL PEOPLE KILLED MONTH-WISE IN INDIA

Column

TOTAL ROAD ACCIDENTS IN INDIA - 2018

Row

AGE AND GENDER WISE TOTAL ACCIDENTS

TRAFFIC VIOLATIONS

Data Table

Pivot Table

MAP

Summary

Row

MAXIMUM ACCIDENTS

63920

MINIMUM ACCIDENTS

3

AVERAGE ACCIDENTS

12973.44

---
title: "Road Accidents in India - 2018"
output: 
  flexdashboard::flex_dashboard:
    theme: cerulean
    source_code: embed
    social: ["twitter","instagram","github","menu"]
    orientation: row
    vertical_layout: fill
---

```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)

library(ggvis)
```


```{r}
##loading dataset
Type_of_junctions<-read.csv( "C:/Users/Tenisha/Documents/acci_data/Accidents Classified According to Type of Junctions during 2018.csv")
month_wise_accidents<-read.csv("C:/Users/Tenisha/Documents/acci_data/monthwise-accidents.csv")
NH_killed<-read.csv("C:/Users/Tenisha/Documents/acci_data/national-highways-killed.csv")
SH_killed<-read.csv("C:/Users/Tenisha/Documents/acci_data/statehighways-killed.csv")
non_wearing_helmet<-read.csv("C:/Users/Tenisha/Documents/acci_data/Road-Accidents-2018-non wearing of helmet.csv")
non_wearing_seatbelt<-read.csv("C:/Users/Tenisha/Documents/acci_data/Non-Use of Safety Device (Non-Wearing of Seat Belt) by Victims during 2018.csv")
place_of_occurrence<-read.csv("C:/Users/Tenisha/Documents/acci_data/place of occurence.csv")
type_of_roads<-read.csv("C:/Users/Tenisha/Documents/acci_data/Road-Accidents-2018--type of roads.csv")
traffic_violations<-read.csv("C:/Users/Tenisha/Documents/acci_data/Road-Accidents-2018-Traffic violations.csv")
weather_conditions<-read.csv("C:/Users/Tenisha/Documents/acci_data/Road-Accidents-2018-weather conditions.csv")

##slicing dataset

type.of.junctions.killed<-Type_of_junctions[c(2,5,11,16,21,26,31)]
NH_killed.persons<-NH_killed[6]
SH_killed.persons<-SH_killed[6]
non.wearing.helmet.killed<-non_wearing_helmet[c(3,8)]
non.wearing.seatbelt<-non_wearing_seatbelt[c(3,8)]
type.of.roads.killed<-type_of_roads[c(5,11,16,21,26,31,36,41)]
traffic.violations.killed<-traffic_violations[c(5,11,16,21,26)]
weather.conditions.killed<-weather_conditions[c(5,11,16,21,26)]


##combining dataset
total.persons.killed.2018<-cbind.data.frame(c(type.of.junctions.killed,NH_killed.persons,SH_killed.persons,non.wearing.helmet.killed,non.wearing.seatbelt,type.of.roads.killed,traffic.violations.killed,weather.conditions.killed))

##combining the sliced dataset
total.persons.killed.2018<-cbind.data.frame(c(type.of.junctions.killed,NH_killed.persons,SH_killed.persons,non.wearing.helmet.killed,non.wearing.seatbelt,type.of.roads.killed,traffic.violations.killed,weather.conditions.killed))


##changing column names
colnames(total.persons.killed.2018)<-c("States.UTs","T.junction","Y.junction","Four.arm.junction","Staggered.junction","Round.about.junction","other.junctions","National.highways","State.highways","Helmet.Drivers","Helmet.Passengers","Seatbelt.Drivers","Seatbelt.Passengers","Straight.Road","Curved.Road","Bridge","Culvert","Pot.holes","Steep.grade","Ongoing.road.work.under.constructions","Other.roads","Over.speeding","Drunken.Driving-consumption.of.Alcohol&Drug","Driving.on.wrongside","Jumping.red.light","Use.of.mobile.phone","sunny.clear","Rainy","Foggy/Misty","Hail.Sleet","Other.weather.conditions")

total.No.Persons.Killed.2018<-total.persons.killed.2018[-37,]


```



```{r}
Type_of_Accidents_2018<-read.csv("C:/Users/Tenisha/Desktop/Accidents_dataset/Road-Accidents-2018--type of accidents in 2018.csv")
age_gender_data<-read.csv("C:/Users/Tenisha/Desktop/Accidents_dataset/Road-Accidents-2018--age_and_gender.csv")
traffic_violations<-read.csv("C:/Users/Tenisha/Desktop/Accidents_dataset/Road-Accidents-2018--type of accidents in 2018.csv")

total.No.Persons.Killed.2018$Total.killed<-rowSums(total.No.Persons.Killed.2018[2:31])
data3<-total.No.Persons.Killed.2018[c(1,32)]
```

Data Visualization {data-orientation=columns}
===================================================================================
Row
-----------------------------------------------------------------------------------
### MAXIMUM KILLED

```{r}
valueBox(max(total.No.Persons.Killed.2018$Total.killed),
         icon = 'fa-analytics')
```

### MINIMUM KILLED

```{r}
valueBox(min(total.No.Persons.Killed.2018$Total.killed),
         icon = 'fa-analytics')
```

### AVERAGE KILLLED

```{r}
valueBox(round(mean(total.No.Persons.Killed.2018$Total.killed),digits=2),
         icon = 'fa-traffic-light-stop')
```

### TOTAL PEOPLE KILLED MONTH-WISE IN INDIA
```{r}
data1<-month_wise_accidents[c(2,5,11,17,23,29,35,41,47,53,59,65,71)]
month.wise.total.killed<-data1[-37,]
library(tidyverse)
month.wise.total.killed$Total.killed<-rowSums(month.wise.total.killed[2:13])
colnames(month.wise.total.killed)<-c("States.UTs","JAN","FEB","MAR","APR","MAY","JUN","JUL","AUG","SEP","OCT","NOV","DEC","TOTAL")
d<-month.wise.total.killed[-c(1,14)]
e<-gather(d)
plot_ly(e,labels = ~e$key,values=e$value,type = 'pie')

```   





Column
------------------------------------------------------------------------------------

### TOTAL ROAD ACCIDENTS IN INDIA - 2018
```{r}
library(tidyverse)
total_accidents<-select(Type_of_Accidents_2018,c("States.UTs","Total.Accidents"))
total_accidents1<-total_accidents[-c(37,38),]
a<-ggplot(data = total_accidents1,aes(reorder(x=States.UTs,Total.Accidents),y=Total.Accidents))+geom_bar(stat="identity",fill="salmon")+coord_flip()+ggtitle("Total Road Accidents in 2018")+xlab("States & Union Territories")+ylab("Total No.of Road Accidents")
ggplotly(a)
```

Row
----------------------------------------------------------------------------------
### AGE AND GENDER WISE TOTAL ACCIDENTS
```{r}
##AGE AND GENDER WISE ROAD ACCIDENT
age_gender_data<-read.csv("C:/Users/Tenisha/Desktop/Accidents_dataset/Road-Accidents-2018--age_and_gender.csv")

age.gender1<-age_gender_data[37,]
age.gender2<-age.gender1[3:16]
age.gender3<-data.frame(t(age.gender2))
age.gender4<-c("lessthan.18-M","lessthan.18-F","18-25.M","18-25.F","25-35.M","25-35.F","35-45.M","35-45.F","45-60.M","45-60.F","60&above.M","60&above.F","Age_notknown.M","Age_notknown.F")
age.gender5<-data.frame(age.gender4,age.gender3$X37)
#barplot
p<-ggplot(age.gender5,aes(reorder(x=age.gender4,age.gender3.X37),y=age.gender3.X37,fill=age.gender4))+geom_bar(stat="identity")+coord_flip()+ggtitle("Age & Gender wise Accidents in 2018")+xlab("Age & Gender")+ylab("No Of Accidents")
ggplotly(p)
        

```

### TRAFFIC VIOLATIONS
```{r}
#TRAFFIC VIOLATIONS
##loading dataset
traffic_violations<-read.csv("C:/Users/Tenisha/Documents/acci_data/Road-Accidents-2018-Traffic violations.csv")
library(dplyr)

##removing unwanted rows and columns
#removing rows which contains total
data1<-traffic_violations[-37,]
##checking missing values



##scatterplot

v<-ggplot(data1,aes(x=Over.Speeding...Persons.Killed...Number,y=Over.Speeding...Persons.Injured...Greviously.Injured))+geom_point(color="red")+geom_line(color="black")
ggplotly(v)
```

------------------------------------------------------------------------------------------


```{r}
total.No.Persons.Killed.2018$Total.killed<-rowSums(total.No.Persons.Killed.2018[2:31])
data3<-total.No.Persons.Killed.2018[c(1,32)]
```

Data Table
=================================================================================================
```{r}
datatable(total.No.Persons.Killed.2018,
          caption = "Total.People.killed.in.Road.Accidents",
          rownames = T,
          filter = "top",
          options = list(pageLength = 25))

```


Pivot Table
=================================================================================================
```{r}

rpivotTable(total.No.Persons.Killed.2018,
            aggregatorName = "Count",
            cols= "fm",
            rows = "State",
            rendererName = "Heatmap")
```


MAP
==================================================================================================



```{r}
library(htmlwidgets)
library(dplyr)
library(highcharter)


hcmap(
  "countries/in/in-all",
  data = data3,
  value = "Total.killed",
  
  name = "Totally.killed",
  dataLabels = list(enabled = TRUE, format = "{point.name}"),
  borderColor = "#FAFAFA",
  borderWidth = 1.0,
  tooltip = list(
  valueDecimals = 2
    
  )
)%>%
            hc_colorAxis(minColor = "lightgreen", maxColor = "red")%>%
            hc_title(text = "Total People Killed in Road Accidents in India - 2018")



```
Summary
===========================================================================================================
Row 
-----------------------------------------------------------------------------------------------------------
### MAXIMUM ACCIDENTS
```{r}
valueBox(max(total_accidents1$Total.Accidents),
         icon = 'fa-traffic-light-stop')
```

### MINIMUM ACCIDENTS

```{r}
valueBox(min(total_accidents1$Total.Accidents),
         icon = 'fa-traffic-light-stop')
```

### AVERAGE ACCIDENTS

```{r}
valueBox(round(mean(total_accidents1$Total.Accidents),digits=2),
         icon = 'fa-traffic-light-stop')
```

-------------------------------------------------------------------------------------------------------------